Insulin-like growth factor 1 (IGF-1) is an important growth factor in childhood. We aimed to investigate the impact of food supplements for the treatment of moderate acute malnutrition (MAM) on serum IGF-1 (sIGF-1). Secondary analysis of a randomised 2 × 2 × 3 factorial nutrition trial was performed. Children aged 6–23 months with MAM received 2093 kJ/d as lipid-based nutrient supplement (LNS) or corn soy blend (CSB), containing either dehulled soya or soya isolate and different quantities of dried skimmed milk (0 %, 20 % or 50 % of total protein) for 12 weeks. The trial was double-blind with regard to soya and milk but not to matrix (LNS v. CSB). sIGF-1 was measured at inclusion and after 12 weeks of supplementation. Of 1609 children enrolled, 1455 (90 %) had sIGF-1 measured at both time points. During supplementation, sIGF-1 increased 6·7 (95 % CI 6·1, 7·3) ng/ml compared with an expected age-dependent decrease of 0·3 (95 % CI 0·2, 0·4) ng/ml. Children who received LNS v. CSB had a lower increase in sIGF-1 (–8 %, 95 % CI − 12, −3). The effect of LNS was partly attenuated when sIGF-1 was corrected for inflammation. Children who received soya isolate compared with dehulled soya had a higher increase in sIGF-1 (6 %, 95 % CI 1, 12). Milk content did not affect sIGF-1. Overall, sIGF-1 increased during supplementation. The lower increase with LNS v. CSB was only partly explained by increased inflammation with LNS and needs further investigation. Isolate v. dehulled soya led to a higher increase which may be due to antinutrients in dehulled soya.

Marine n-3 fatty acids (n-3LCPUFA) have shown neurocognitive benefits in children with attention-deficit/hyperactivity disorder (ADHD), but few trials have examined effects in adults with autism spectrum disorder (ASD). We explored, if n-3LCPUFA affect cognitive functions in adults with ASD, and if effects are modified by comorbid ADHD. In a 2 × 4 week crossover study, twenty-six participants were randomised to sequence of supplementation with fish oil (FO, 5·2 g/d n-3PUFA) and safflower oil (SO). At baseline and after each period, we measured primary outcomes: attention (d2-test) and spatial working memory (Corsi test) and secondary outcomes: flexibility (Stroop word-colour test), ADHD symptoms (Conners scales), executive functions (Behavioural Inventory of Executive Function) and social behaviour (Social Responsiveness Scale). The dropout rate was 15 %. Compliance was 94 % and correlated with whole-blood n-3LCPUFA. Corsi scores improved by ∼0·3 × sd (P = 0·032) after FO v. SO, and the odds for d2 errors were 30 % lower (P = 0·016), which was supported by improved Conners scores of attention (P = 0·023). Improvement in Conners ADHD symptom score was limited to participants with ADHD (–3·5(–6·0; –1·0), n 10 v. −0·2(–2·5;2·2), n 11 without ADHD, P interaction = 0·096), who also improved their behavioural regulation index by 0·3 × sd after FO (P interaction = 0·016). Participants without ADHD gained most in d2 test performance (OR = 0·4(0·2;0·7) v. 0·9(0·6;1·3) in those with ADHD, P interaction = 0·002), but their executive function score was exacerbated after FO (5·9(0·0,11·8), P interaction = 0·039). Our results did not show any effects on ASD symptoms, but suggest that FO may improve attention and working memory in adults with ASD and ameliorate ADHD symptoms in those with comorbid ADHD.

Germination and emergence assays represent the most notable examples of time-to-event data in agriculture and related disciplines. In spite of the peculiar characteristics of this type of data, there has been little effort to establish a specific and comprehensive framework for their analyses. Indeed, a brief survey of the literature shows that germination and emergence data, along with other phenological measurements such as flowering time, have been analyzed through myriad approaches, giving rise to confusion and uncertainty among scientists and practitioners as to what may represent the best statistical practice. This lack of coherence in statistical approach may reduce the efficiency of research, while making the communication of results and the cross-study comparisons extremely challenging. Here, we attempt to provide a coherent framework and protocol for the analyses of germination/emergence and other time-to-event data in weed science and related disciplines, together with a software implementation in the form of a new R package. We propose a similar approach to biological assays in ecotoxicology, based on: (1) fitting a time-to-event model to describe the whole time course of events; (2) comparing time-to-event curves across experimental treatments, and (3) deriving further information from the fitted model to better focus on some traits of interest. The most appropriate methods to accomplish this procedure were carefully selected from the framework of survival analysis and related sources and were modified to comply with the specific needs of weed, seed, and plant sciences. Finally, they were implemented in the new R package drcte. In this article, we describe the procedure and its limitations by way of providing examples of several types of germination/emergence assays. We highlight that our proposed procedure can also serve as the first step of data analyses, with its output subsequently submitted to traditional or meta-analytic approaches.

Personalised nutrition (PN) is an emerging field that bears great promise. Several definitions of PN have been proposed and different modelling approaches have been used to claim PN effects. We tentatively propose to group these approaches into two categories, which we term outcome-based and population reference approaches, respectively. Understanding the fundamental differences between these two types of modelling approaches may allow a more realistic appreciation of what to expect from PN interventions presently and may be helpful for designing and planning future studies investigating PN interventions.

Germination experiments are becoming increasingly complex and they are now routinely involving several experimental factors. Recently, a two-step approach utilizing meta-analysis methodology has been proposed for the estimation of hierarchical models suitable for describing data from such complex experiments. Step 1 involves fitting models to data from each sub-experiment, whereas Step 2 involves combination estimates from all model fits obtained in Step 1. However, one shortcoming of this approach was that visualization of resulting fitted germination curves was difficult. Here, we describe in detail an improved two-step analysis that allows visualization of cumulated data together with fitted curves and confidence bands. Also, we demonstrate in detail, through two examples, how to carry out the statistical analysis in practice.

New dietary-based concepts are needed for treatment and effective prevention of overweight and obesity. The primary objective was to investigate if reduction in appetite is associated with improved weight loss maintenance. This cohort study was nested within the European Commission project Satiety Innovation (SATIN). Participants achieving ≥8% weight loss during an initial 8-week low-energy formula diet were included in a 12-week randomised double-blind parallel weight loss maintenance intervention. The intervention included food products designed to reduce appetite or matching controls along with instructions to follow national dietary guidelines. Appetite was assessed by ad libitum energy intake and self-reported appetite evaluations using visual analogue scales during standardised appetite probe days. These were evaluated at the first day of the maintenance period compared with baseline (acute effects after a single exposure of intervention products) and post-maintenance compared with baseline (sustained effects after repeated exposures of intervention products) regardless of randomisation. A total of 181 participants (forty-seven men and 134 women) completed the study. Sustained reduction in 24-h energy intake was associated with improved weight loss maintenance (R 0·37; P = 0·001), whereas the association was not found acutely (P = 0·91). Suppression in self-reported appetite was associated with improved weight loss maintenance both acutely (R −0·32; P = 0·033) and sustained (R −0·33; P = 0·042). Reduction in appetite seems to be associated with improved body weight management, making appetite-reducing food products an interesting strategy for dietary-based concepts.

A low-energy diet (LED) is an effective approach to induce a rapid weight loss in individuals with overweight. However, reported disproportionally large losses of fat-free mass (FFM) after an LED trigger the question of adequate protein content. Additionally, not all individuals have the same degree of weight loss success. After an 8-week LED providing 5020 kJ/d for men and 4184 kJ/d for women (84/70 g protein/d) among overweight and obese adults, we aimed to investigate the relationship between protein intake relative to initial FFM and proportion of weight lost as FFM as well as the individual characteristics associated with weight loss success. We assessed all outcomes baseline and after the LED. A total of 286 participants (sixty-four men and 222 women) initiated the LED of which 82 % completed and 70 % achieved a substantial weight loss (defined as ≥8 %). Protein intake in the range 1·0–1·6 g protein/d per kg FFM at baseline for men and 1·1–2·2 g protein/d per kg FFM at baseline for women was not associated with loss of FFM (P = 0·632). Higher Three-Factor Eating Questionnaire (TFEQ) hunger at baseline and reductions in TFEQ disinhibition and hunger during the LED were associated with larger weight loss (all P ≤ 0·020); whereas lower sleep quality at baseline predicted less successful weight loss using intention to treat analysis (P = 0·021), possibly driven by those dropping out (n 81, P = 0·067 v. completers: n 198, P = 0·659). Thus, the protein intakes relative to initial FFM were sufficient for maintenance of FFM and specific eating behaviour characteristics were associated with weight loss success.

The study aimed at assessing stunting, wasting and breast-feeding as correlates of body composition in Cambodian children. As part of a nutrition trial (ISRCTN19918531), fat mass (FM) and fat-free mass (FFM) were measured using 2H dilution at 6 and 15 months of age. Of 419 infants enrolled, 98 % were breastfed, 15 % stunted and 4 % wasted at 6 months. At 15 months, 78 % were breastfed, 24 % stunted and 11 % wasted. Those not breastfed had lower FMI at 6 months but not at 15 months. Stunted children had lower FM at 6 months and lower FFM at 6 and 15 months compared with children with length-for-age z ≥0. Stunting was not associated with height-adjusted indexes fat mass index (FMI) or fat-free mass index (FFMI). Wasted children had lower FM, FFM, FMI and FFMI at 6 and 15 months compared with children with weight-for-length z (WLZ) ≥0. Generally, FFM and FFMI deficits increased with age, whereas FM and FMI deficits decreased, reflecting interactions between age and WLZ. For example, the FFM deficits were –0·99 (95 % CI –1·26, –0·72) kg at 6 months and –1·44 (95 % CI –1·69; –1·19) kg at 15 months (interaction, P<0·05), while the FMI deficits were –2·12 (95 % CI –2·53, –1·72) kg/m2 at 6 months and –1·32 (95 % CI –1·77, –0·87) kg/m2 at 15 months (interaction, P<0·05). This indicates that undernourished children preserve body fat at the detriment of fat-free tissue, which may have long-term consequences for health and working capacity.

Early nutrition and growth have been found to be important early exposures for later development. Studies of crude growth in terms of weight and length/height, however, cannot elucidate how body composition (BC) might mediate associations between nutrition and later development. In this study, we aimed to examine the relation between fat mass (FM) or fat-free mass (FFM) tissues at birth and their accretion during early infancy, and later developmental progression. In a birth cohort from Ethiopia, 455 children who have BC measurement at birth and 416 who have standardised rate of BC growth during infancy were followed up for outcome variable, and were included in the statistical analysis. The study sample was restricted to mothers living in Jimma town who gave birth to a term baby with a birth weight ≥1500 g and no evident congenital anomalies. The relationship between the exposure and outcome variables was examined using linear-mixed regression model. The finding revealed that FFM at birth was positively associated with global developmental progression from 1 to 5 years (β=1·75; 95 % CI 0·11, 3·39) and from 4 to 5 years (β=1·34; 95 % CI 0·23, 2·44) in the adjusted model. Furthermore, the rate of postnatal FFM tissue accretion was positively associated with development at 1 year of age (β=0·50; 95 % CI 0·01, 0·99). Neither fetal nor postnatal FM showed a significant association. In conclusion, fetal, rather than postnatal, FFM tissue accretion was associated with developmental progression. Intervention studies are needed to assess whether nutrition interventions increasing FFM also increase cognitive development.

Manipulation of food's macronutrient composition in order to reduce energy content without compromising satiating capacity may be helpful in body weight control. For cheeses, substituting fat with protein may provide such opportunity. We aimed at examining the acute effect of cheeses with different macronutrient compositions on accumulated energy intake and subjective appetite sensation. A total of thirty-nine normal-weight (average BMI 24·4 kg/m2) men and women completed the partly double-blind, randomised crossover study with high-protein/low-fat (HP/LF, 696 kJ), high-protein/high-fat (HP/HF, 976 kJ) and low-protein/high-fat (LP/HF, 771 kJ) cheeses. After overnight fasting, 80 g cheese were served with 70 g bread, 132 g juice and 125 g coffee/tea/water. Ad libitum spaghetti bolognaise was served after 3 h and energy intake assessed. Subjective appetite ratings were assessed using visual analogue scales. Composite appetite scores were calculated and evaluated relatively to energy intake. Total accumulated energy intake was 188·3 (se 97·4) kJ lower when consuming the HP/LF compared with the HP/HF (P ≤ 0·05), but, compared with the LP/HF cheese, the difference was not significant (177·0 (se 100·4) kJ lower; P = 0·08). In relation to energy intake, the composite appetite score was lower when consuming the HP/LF compared with the HP/HF (P = 0·003) and the LP/HF (P = 0·007) cheeses. Thereby, no compensatory eating following consumption of the HP/LF compared with the HP/HF cheese was found. The HP/LF cheese resulted in an increased feeling of satiety in relation to its lower energy content compared with both HP/HF and LP/HF cheeses.

In recent years germination experiments have become more and more complex. Typically, they are replicated in time as independent runs and at each time point they involve hierarchical, often factorial experimental designs, which are now commonly analysed by means of linear mixed models. However, in order to characterize germination in response to time elapsed, specific event-time models are needed and mixed model extensions of these models are not readily available, neither in theory nor in practice. As a practical workaround we propose a two-step approach that combines and weighs together results from event-time models fitted separately to data from each germination test by means of meta-analytic random effects models. We show that this approach provides a more appropriate appreciation of the sources of variation in hierarchically structured germination experiments as both between- and within-experiment variation may be recovered from the data.

In a longitudinal study including 642 healthy 8–11-year-old Danish children, we investigated associations between vitamin D dependent SNP and serum 25-hydroxyvitamin D (25(OH)D) concentrations across a school year (August–June). Serum 25(OH)D was measured three times for every child, which approximated measurements in three seasons (autumn, winter, spring). Dietary and supplement intake, physical activity, BMI and parathyroid hormone were likewise measured at each time point. In all, eleven SNP in four vitamin D-related genes: Cytochrome P450 subfamily IIR1 (CYP2R1); 7-dehydrocholesterol reductase/nicotinamide adenine dinucleotide synthetase-1(DHCR7/NADSYN1); group-specific complement (GC); and vitamin D receptor were genotyped. We found minor alleles of CYP2R1 rs10500804, and of GC rs4588 and rs7041 to be associated with lower serum 25(OH)D concentrations across the three seasons (all P<0·01), with estimated 25(OH)D differences of −5·8 to −10·6 nmol/l from major to minor alleles homozygosity. In contrast, minor alleles homozygosity of rs10741657 and rs1562902 in CYP2R1 was associated with higher serum 25(OH)D concentrations compared with major alleles homozygosity (all P<0·001). Interestingly, the association between season and serum 25(OH)D concentrations was modified by GC rs7041 (Pinteraction=0·044), observed as absence of increase in serum 25(OH)D from winter to spring among children with minor alleles homozygous genotypes compared with the two other genotypes of rs7041 (P<0·001). Our results suggest that common genetic variants are associated with lower serum 25(OH)D concentrations across a school year. Potentially due to modified serum 25(OH)D response to UVB sunlight exposure. Further confirmation and paediatric studies investigating vitamin D-related health outcomes of these genotypic differences are needed.

As cases of resistance to herbicides escalate worldwide, there is increasing demand from growers to test for weed resistance and learn how to manage it. Scientists have developed resistance-testing protocols for numerous herbicides and weed species. Growers need immediate answers and scientists are faced with the daunting task of testing an increasingly large number of samples across a variety of species and herbicides. Quick tests have been, and continue to be, developed to address this need, although classical tests are still the norm. Newer methods involve molecular techniques. Whereas the classical whole-plant assay tests for resistance regardless of the mechanism, many quick tests are limited by specificity to an herbicide, mode of action, or mechanism of resistance. Advancing knowledge in weed biology and genomics allows for refinements in sampling and testing protocols. Thus, approaches in resistance testing continue to diversify, which can confound the less experienced. We aim to help weed science practitioners resolve questions pertaining to the testing of herbicide resistance, starting with field surveys and sampling methods, herbicide screening methods, data analysis, and, finally, interpretation. More specifically, this article discusses approaches for sampling plants for resistance confirmation assays, provides brief overviews on the biological and statistical basis for designing and analyzing dose–response tests, and discusses alternative procedures for rapid resistance confirmation, including molecular-based assays. Resistance confirmation procedures often need to be slightly modified to suit a specific situation; thus, the general requirements as well as pros and cons of quick assays and DNA-based assays are contrasted. Ultimately, weed resistance testing research, as well as resistance management decisions arising from research, needs to be practical, feasible, and grounded in science-based methods.

Advances in statistical software allow statistical methods for nonlinear regression analysis of dose-response curves to be carried out conveniently by non-statisticians. One such statistical software is the program R with the drc extension package. The drc package can: (1) simultaneously fit multiple dose-response curves; (2) compare curve parameters for significant differences; (3) calculate any point along the curve at the response level of interest, commonly known as an effective dose (e.g., ED30, ED50, ED90), and determine its significance; and (4) generate graphs for publications or presentations. We believe that the drc package has advantages that include: the ability to relatively simply and quickly compare multiple curves and select ED-levels easily along the curve with relevant statistics; the package is free of charge and does not require licensing fees, and the size of the package is only 70 MB. Therefore, our objectives are to: (1) provide a review of a few common issues in dose-response-curve fitting, and (2) facilitate the use of up-to-date statistical techniques for analysis of dose-response curves with this software. The methods described can be utilized to evaluate chemical and non-chemical weed control options. Benefits to the practitioners and academics are also presented.

There are various reasons for using statistics, but perhaps the most important is that the biological sciences are empirical sciences. There is always an element of variability that can only be dealt with by applying statistics. Essentially, statistics is a way to summarize the variability of data so that we can confidently say whether there is a difference among treatments or among regression parameters and tell others about the variability of the results. To that end, we must use the most appropriate statistics to get a “correct” picture of the experimental variability, and the best way of doing that is to report the size of the parameters or the means and their associated standard errors or confidence intervals. Simply declaring that the yields were 1 or 2 ton ha−1 does not mean anything without associated standard errors for those yields. Another driving force is that no journal will accept publications without the data having been subjected to some kind of statistical analysis.

This article discusses the concept of relative potency of herbicides in bioassays where individual dose–response curves can be similar or nonsimilar, often denoted parallel and nonparallel curves, and have different upper and lower limits. The relative potency is constant for similar dose–response curves and measures the relative horizontal displacement of curves of a similar shape along the dose axis on a logarithmic scale. The concept of similar dose–response curves has been used extensively to assess results from herbicide experiments, for example, with the purpose of adjusting herbicide doses to environmental conditions, formulations, and adjuvants. However, deeming dose–response curves similar when they are not may greatly affect the calculation of the relative potency at response levels such as effective dosage (ED)90, which is relevant for effective weed control, or ED 10, which is used in crop tolerance studies. We present a method for calculating relative potencies between nonsimilar dose–response curves at any response level. It also is demonstrated that if the upper, lower, or both limits among response curves are substantially different, then the ED 50 or any other ED level cannot be used indiscriminately to compute the relative potency. Rather, the relative potency should be viewed as a function of the response level.

Although foliar herbicide absorption has been studied intensively, there is currently no standardized method for data analysis when evaluating herbicide absorption over time. Most peer-reviewed journals require the treatment structure of data be incorporated in the analysis; however, many herbicide absorption studies published in the past 5 yr do not account for the time structure of the experiment. Herbicide absorption studies have been presented in a variety of ways, making it difficult to compare results among studies. The objective of this article is to propose possible nonlinear models to analyze herbicide absorption data and to provide a stepwise framework so that researchers may standardize the analysis method in this important research area. Asymptotic regression and rectangular hyperbolic models with similar parameterizations are proposed, so that the maximum herbicide absorption and absorption rate may be adequately modeled and statistically compared among treatments. Adoption of these models for herbicide absorption analysis over time will provide a standardized method making comparison of results within and among studies more practical.